References

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Media

  • Boukabara, S. (2019, April 17). AI for Earth observation and numerical weather prediction. Retrieved May 26, 2019, from https://spacenews.com/ai-for-earth-observation-and-numerical-weather-prediction/
  • Crowder, L. (2018, June 28). AI and climate: On the "bleeding edge" with a pioneering researcher. Retrieved May 26, 2019, from https://thebulletin.org/2018/02/ai-and-climate-on-the-bleeding-edge-with-a-pioneering-researcher/
  • Davenport, C., & Landler, M. (2019, May 27). Trump Administration Hardens Its Attack on Climate Science. The New York Times. Retrieved June 23, 2019, from https://www.nytimes.com/2019/05/27/us/politics/trump-climate-science.html
  • Evarts, H. (2018, June 19). Machine Learning May Be a Game-Changer for Climate Prediction. Retrieved May 26, 2019, from https://engineering.columbia.edu/press-releases/machine-learning-climate-prediction
  • Koetsier, J. (2019, July 18). Viral App FaceApp Now Owns Access To More Than 150 Million People's Faces And Names. Retrieved July 19, 2019, from https://www.forbes.com/sites/johnkoetsier/2019/07/17/viral-app-faceapp-now-owns-access-to-more-than-150-million-peoples-faces-and-names/#20deb73c62f1
  • Perkins, R. (2018, December 12). New Climate Model to Be Built from the Ground Up. Retrieved June 23, 2019, from https://www.caltech.edu/about/news/new-climate-model-be-built-ground-84636
  • Roach, J. (2019, May 20). Researchers turn to AI in a bid to improve weather forecasts. Retrieved May 26, 2019, from https://blogs.microsoft.com/ai/ai-subseasonal-weather-forecast/
  • Voosen, P. (2019, May 22). Science insurgents plot a climate model driven by artificial intelligence. Retrieved May 26, 2019, from https://www.sciencemag.org/news/2018/07/science-insurgents-plot-climate-model-driven-artificial-intelligence

Research Paper

  • Bue, B. D., Thompson, D. R., Deshpande, S., Eastwood, M., Green, R. O., Mullen, T., . . . Parente, M. (2019). Neural Network Radiative Transfer for Imaging Spectroscopy. Atmospheric Measurement Techniques Discussions, 1-16. doi:10.5194/amt-2018-436
  • Dash, Y, Mishra, SK, Panigrahi, BK. Predictability assessment of northeast monsoon rainfall in India using sea surface temperature anomaly through statistical and machine learning techniques. Environmetrics. 2019; 30:e2533. https://doi.org/10.1002/env.2533
  • Deshpande, S., Bue, B. D., Thompson, D. R., Natraj, V., & Parente, M. (2019). Learning Radiative Transfer Models for Climate Change Applications in Imaging Spectroscopy. arXiv preprint arXiv:1906.03479.
  • Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., & Yacalis, G. ( 2018). Could machine learning break the convection parameterization deadlock? Geophysical Research Letters, 45, 5742– 5751. https://doi.org/10.1029/2018GL078202
  • Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., ... & Luccioni, A. (2019). Tackling Climate Change with Machine Learning. arXiv preprint arXiv:1906.05433.
  • Scher, S., & Messori, G. (2018). Predicting weather forecast uncertainty with machine learning. Quarterly Journal of the Royal Meteorological Society, 144(717), 2830-2841.
  • Schmidt, V., Luccioni, A., Mukkavilli, S. K., Balasooriya, N., Sankaran, K., Chayes, J., & Bengio, Y. (2019). Visualizing the consequences of climate change using cycle-consistent adversarial networks. arXiv preprint arXiv:1905.03709.
  • Wang, B., Zheng, L., Liu, D., Ji, F., Clark, A., & Yu, Q. (2018). Using multi‐model ensembles of CMIP5 global climate models to reproduce observed monthly rainfall and temperature with machine learning methods in Australia. International Journal of Climatology, 38(13), 4891-4902.

Others

  • American Meteorological Society, cited 2012: Parameterization. Glossary of Meteorology. [Available online at http://glossary.ametsoc.org/wiki/parameterization.]
  • Pickett, M., Perkins, R., & Perkins, R. (n.d.). Home. Retrieved June 23, 2019, from https://clima.caltech.edu/
  • Wel, N. V. (2019, June 18). 40th Session of the Joint Scientific Committee. Retrieved from https://www.wcrp-climate.org/jsc40-about

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